Development and Evaluation of Computational Method for Korean Threshold Runoff

Cho, Bae-Gun; Ji, Hee-Sook; Bae, Deg‐Hyo

doi:10.3741/jkwra.2011.44.11.875

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…where α, β, γ and δ are regression coefficients. significant negative correlation with the average basin slope S. Conversely, the hydraulic depth H was negatively correlated with A but positively correlated with L and S. The local channel slope S c was negatively correlated with A and L. The derived regression equations are also shown in Table 3, and the determination coefficients of the regression equation were 0.76, 0.37 and 0.53 (Cho et al, 2011). The determination coefficient of hydraulic depth (H ) is lower than the other variables.…”

Section: Regional Regression Relationships Based On Channel Geometrymentioning

confidence: 90%

Revision according to the referee’s comments

Bae¹

2017

Preprint

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We thank you for your constructive review and comments. We have attached answers and the revised manuscript. Our responses were written after this symbol (-). We believe that the manuscript is in much better form now.Referee's comments: Important topic of high relevance. The paper is generally of a good structure and gives good insight in what has been done in the project. But not really a new approach, already been done in similar ways in other regions. Following open issues should be addressed in the publication: The cells of convective events are much smaller than the catchments described in the paper, so in the real world only part of the catchment C1

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Section: Regional Regression Relationships Based On Channel Geometrymentioning

confidence: 90%

Revision according to the referee’s comments

Bae¹

2017

Preprint

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“…The derived regression equations are also shown in Table 3, and the determination coefficients of the regression equation were 0.76, 0.37 and 0.53 (Cho et al, 2011). The determination coefficient of hydraulic depth (H ) is lower than the other variables.…”

Section: Regional Regression Relationships Based On Channel Geometrymentioning

confidence: 99%

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Bae

Lee

Moon

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents quantitative criteria for flash flood warning that can be used to rapidly assess flash flood occurrence based on only rainfall estimates. This study was conducted for 200 small mountainous sub-catchments of the Han River basin in South Korea because South Korea has recently suffered many flash flood events. The quantitative criteria are calculated based on flash flood guidance (FFG), which is defined as the depth of rainfall of a given duration required to cause frequent flooding (1-2-year return period) at the outlet of a small stream basin and is estimated using threshold runoff (TR) and antecedent soil moisture conditions in all sub-basins. The soil moisture conditions were estimated during the flooding season, i.e., July, August and September, over 7 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) using the Sejong University Rainfall Runoff (SURR) model. A ROC (receiver operating characteristic) analysis was used to obtain optimum rainfall values and a generalized precipitation-area (P -A) curve was developed for flash flood warning thresholds. The threshold function was derived as a P -A curve because the precipitation threshold with a short duration is more closely related to basin area than any other variables. For a brief description of the P -A curve, generalized thresholds for flash flood warnings can be suggested for rainfall rates of 42, 32 and 20 mm h −1 in sub-basins with areas of 22-40, 40-100 and > 100 km 2 , respectively. The proposed P -A curve was validated based on observed flash flood events in different sub-basins. Flash flood occurrences were captured for 9 out of 12 events. This result can be used instead of FFG to identify brief flash flood (less than 1 h), and it can provide warning information to decision-makers or citizens that is relatively simple, clear and immediate.

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“…Ref [9] analyzed the relationship between flash flood index and runoff number characteristics to develop an equation between the two. Ref [10] proposed a threshold runoff calculation method using the flash flood guidance (FFG) model, which is more suitable for Korea rather than those used in the United States. The researchers presented the method as a way to acquire basic data for a flash flood forecast system.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Threshold Rainfall in Ungauged Areas Using Machine Learning

Choo

Choi

et al. 2022

Water

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In recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area (A), mean drainage elevation (H), mean drainage slope (S), drainage density (D), runoff curve number (CN), watershed parameter (Lp), and form factor (Rs) etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport (MOLIT) was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method (SVM), the random forest method, and eXtreme Gradient Boosting (XGBoost). As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE 0.28, and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding.

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Development and Evaluation of Computational Method for Korean Threshold Runoff

Cited by 5 publications

References 5 publications

Revision according to the referee’s comments

Revision according to the referee’s comments

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Estimation of Threshold Rainfall in Ungauged Areas Using Machine Learning

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